Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to
heat and particle flux constraints on the divertor target. Simulation is an important tool to …

Machine learning has had an enormous impact in many scientific disciplines. It has also
attracted significant interest in the field of low-temperature plasma (LTP) modeling and …

Deep reinforcement learning (DRL) has shown significant promise for uncovering
sophisticated control policies that interact in environments with complicated dynamics, such …

Density ramps with ion grad B drift directed into lower single null KSTAR L-mode plasmas
are associated with a simultaneous and abrupt reduction of the divertor particle flux on both …

In this paper, we consider the problem of discovering dynamical system models from noisy
data. The presence of noise is known to be a significant problem for symbolic regression …

Achieving precise control over self-propelled undulatory swimmers requires a deep
understanding of their intricate dynamics. This paper presents a method for addressing …

The predicted divertor conditions for the SPARC tokamak are calculated using SOLPS-ITER
for a range of scrape-off-layer (SOL) heat flux widths \lamq, input powers, and particle …

Reduced order models are becoming increasingly important for rendering complex and
multiscale spatio-temporal dynamics computationally tractable. The computational efficiency …

The drive to control tokamaks, a prominent technology in nuclear fusion, is essential due to
its potential to provide a virtually unlimited source of clean energy. Reinforcement learning …

Many scientific problems can be formulated as sparse regression, ie, regression onto a set
of parameters when there is a desire or expectation that some of the parameters are exactly …